

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_UpperCamelCase					     =						logging.get_logger(__name__)







class     __lowercase (snake_case__					):
 _UpperCamelCase         =    ["""input_features""", """attention_mask"""]
 def __init__(				self ,				A_=80 ,				A_=1_6000 ,				A_=0.0 ,				A_=10 ,				A_=25 ,				A_="hamming_window" ,				A_=3_2768.0 ,				A_=0.97 ,				A_=1.0 ,				A_=True ,				A_=True ,				A_=False ,				**A_ ,				)   ->str:



       '''simple docstring'''





       super().__init__(feature_size=__snake_case ,				sampling_rate=__snake_case ,				padding_value=__snake_case ,				**__snake_case   )

       __lowerCAmelCase   :		Optional[int]   =   feature_size
       __lowerCAmelCase   :		Any   =   sampling_rate
       __lowerCAmelCase   :		Tuple   =   padding_value
       __lowerCAmelCase   :		Optional[Any]   =   hop_length
       __lowerCAmelCase   :		List[str]   =   win_length
       __lowerCAmelCase   :		Union[str, Any]   =   frame_signal_scale
       __lowerCAmelCase   :		List[Any]   =   preemphasis_coeff
       __lowerCAmelCase   :		Union[str, Any]   =   mel_floor
       __lowerCAmelCase   :		Dict   =   normalize_means
       __lowerCAmelCase   :		Union[str, Any]   =   normalize_vars
       __lowerCAmelCase   :		List[Any]   =   win_function
       __lowerCAmelCase   :		Union[str, Any]   =   return_attention_mask

       __lowerCAmelCase   :		Optional[int]   =   win_length * sampling_rate // 1000
       __lowerCAmelCase   :		List[str]   =   hop_length * sampling_rate // 1000

       __lowerCAmelCase   :		List[str]   =   optimal_fft_length(self.sample_size   )
       __lowerCAmelCase   :		str   =   (self.n_fft // 2) + 1
 def       UpperCamelCase__    (				self ,				A_   )   ->Tuple:



       '''simple docstring'''





       if self.win_function == "hamming_window":
             __lowerCAmelCase   :		Any   =   window_function(window_length=self.sample_size ,				name=self.win_function ,				periodic=__snake_case   )
       else:
             __lowerCAmelCase   :		int   =   window_function(window_length=self.sample_size ,				name=self.win_function   )

       __lowerCAmelCase   :		int   =   mel_filter_bank(
           num_frequency_bins=self.n_freqs ,				num_mel_filters=self.feature_size ,				min_frequency=0.0 ,				max_frequency=self.sampling_rate / 2.0 ,				sampling_rate=self.sampling_rate ,				)

       __lowerCAmelCase   :		Union[str, Any]   =   spectrogram(
           one_waveform * self.frame_signal_scale ,				window=__snake_case ,				frame_length=self.sample_size ,				hop_length=self.sample_stride ,				fft_length=self.n_fft ,				center=__snake_case ,				preemphasis=self.preemphasis_coeff ,				mel_filters=__snake_case ,				mel_floor=self.mel_floor ,				log_mel='''log''' ,				)
       return msfc_features.T
 def       UpperCamelCase__    (				self ,				A_ ,				A_ ,				A_   )   ->Tuple:



       '''simple docstring'''





       if self.normalize_means:
             __lowerCAmelCase   :		Tuple   =   x[:input_length].mean(axis=0   )
             __lowerCAmelCase   :		Any   =   np.subtract(__snake_case ,				__snake_case   )
       if self.normalize_vars:
             __lowerCAmelCase   :		List[str]   =   x[:input_length].std(axis=0   )
             __lowerCAmelCase   :		str   =   np.divide(__snake_case ,				__snake_case   )

       if input_length < x.shape[0]:
             __lowerCAmelCase   :		Dict   =   padding_value

       # make sure array is in float32
       __lowerCAmelCase   :		Dict   =   x.astype(np.floataa   )

       return x
 def       UpperCamelCase__    (				self ,				A_ ,				A_ = None   )   ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[int]   =   attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(__snake_case ,				__snake_case ,				self.padding_value   ) for x, n in zip(__snake_case ,				__snake_case   )]





 def __call__(				self ,				A_ ,				A_ = False ,				A_ = None ,				A_ = False ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				**A_ ,				)   ->List[str]:



       '''simple docstring'''





       if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                       f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
                       f""" {self.sampling_rate} and not {sampling_rate}."""   )
       else:
             logger.warning(
                 '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''   )

       __lowerCAmelCase   :		Any   =   isinstance(__snake_case ,				np.ndarray   ) and len(raw_speech.shape   ) > 1
       if is_batched_numpy and len(raw_speech.shape   ) > 2:
             raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""   )
       __lowerCAmelCase   :		Any   =   is_batched_numpy or (
           isinstance(__snake_case ,				(list, tuple)   ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)   ))
       )

       if is_batched:
             __lowerCAmelCase   :		str   =   [np.asarray(__snake_case ,				dtype=np.floataa   ) for speech in raw_speech]
       elif not is_batched and not isinstance(__snake_case ,				np.ndarray   ):
             __lowerCAmelCase   :		Union[str, Any]   =   np.asarray(__snake_case ,				dtype=np.floataa   )
       elif isinstance(__snake_case ,				np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
             __lowerCAmelCase   :		List[Any]   =   raw_speech.astype(np.floataa   )

       # always return batch
       if not is_batched:
             __lowerCAmelCase   :		List[Any]   =   [raw_speech]

       # extract fbank features
       __lowerCAmelCase   :		List[str]   =   [self._extract_mfsc_features(__snake_case   ) for one_waveform in raw_speech]

       # convert into correct format for padding
       __lowerCAmelCase   :		List[Any]   =   BatchFeature({'''input_features''': features}   )

       __lowerCAmelCase   :		int   =   self.pad(
           __snake_case ,				padding=__snake_case ,				max_length=__snake_case ,				truncation=__snake_case ,				pad_to_multiple_of=__snake_case ,				return_attention_mask=__snake_case ,				**__snake_case ,				)
       # make sure list is in array format
       __lowerCAmelCase   :		Optional[int]   =   padded_inputs.get('''input_features'''   )
       if isinstance(input_features[0] ,				__snake_case   ):
             __lowerCAmelCase   :		Any   =   [np.asarray(__snake_case ,				dtype=np.floataa   ) for feature in input_features]

       __lowerCAmelCase   :		Optional[int]   =   padded_inputs.get('''attention_mask'''   )
       if attention_mask is not None:
             __lowerCAmelCase   :		Tuple   =   [np.asarray(__snake_case ,				dtype=np.intaa   ) for array in attention_mask]

       if self.normalize_means or self.normalize_vars:
             __lowerCAmelCase   :		str   =   (
                 np.array(__snake_case ,				dtype=np.intaa   )
                 if self._get_padding_strategies(__snake_case ,				max_length=__snake_case   ) is not PaddingStrategy.DO_NOT_PAD
                 and padding
                 else None
             )
             __lowerCAmelCase   :		str   =   self.normalize(
                 padded_inputs['''input_features'''] ,				attention_mask=__snake_case   )

       if return_tensors is not None:
             __lowerCAmelCase   :		Tuple   =   padded_inputs.convert_to_tensors(__snake_case   )

       return padded_inputs






from math import factorial

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							real
         if isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[1] * rank
         else:
              snake_case											=							rank
    def __repr__(			self       ):
         return (
             F'''{self.real}+'''
             F'''{"+".join(str(__snake_case       )+"E"+str(n+1       )for n,dual in enumerate(self.duals       )       )}'''
         )
    def 						a_     (			self       ):
         snake_case											=							self.duals.copy()
         while cur[-1] == 0:
              cur.pop(-1       )
         return Dual(self.real     ,   __snake_case       )
    def __add__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              return Dual(self.real + other     ,   self.duals       )
         snake_case											=							self.duals.copy()
         snake_case											=							other.duals.copy()
         if len(__snake_case       ) > len(__snake_case       ):
              o_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         elif len(__snake_case       ) < len(__snake_case       ):
              s_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         snake_case											=							[]
         for i in range(len(__snake_case       )       ):
              new_duals.append(s_dual[i] + o_dual[i]       )
         return Dual(self.real + other.real     ,   __snake_case       )

    __magic_name__         =						__add__
    def __sub__(			self     ,   __snake_case       ):
         return self + other * -1
    def __mul__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i * other       )
              return Dual(self.real * other     ,   __snake_case       )
         snake_case											=							[0] * (len(self.duals       ) + len(other.duals       ) + 1)
         for i, item in enumerate(self.duals       ):
              for j, jtem in enumerate(other.duals       ):
                   new_duals[i + j + 1] += item * jtem
         for k in range(len(self.duals       )       ):
              new_duals[k] += self.duals[k] * other.real
         for index in range(len(other.duals       )       ):
              new_duals[index] += other.duals[index] * self.real
         return Dual(self.real * other.real     ,   __snake_case       )

    __magic_name__         =						__mul__
    def __truediv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i / other       )
              return Dual(self.real / other     ,   __snake_case       )
         raise ValueError
    def __floordiv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i // other       )
              return Dual(self.real // other     ,   __snake_case       )
         raise ValueError




    def __pow__(			self     ,   __snake_case       ):
         if n < 0 or isinstance(__snake_case     ,   __snake_case       ):
              raise ValueError('''power must be a positive integer'''       )
         if n == 0:
              return 1
         if n == 1:
              return self
         snake_case											=							self
         for _ in range(n - 1       ):
              x *= self
         return x






def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     if not callable(UpperCamelCase_							):
          raise ValueError('''differentiate() requires a function as input for func'''							)
     if not isinstance(UpperCamelCase_				,(float, int)							):
          raise ValueError('''differentiate() requires a float as input for position'''							)
     if not isinstance(UpperCamelCase_				,UpperCamelCase_							):
          raise ValueError('''differentiate() requires an int as input for order'''							)
     snake_case											=							Dual(UpperCamelCase_				,1							)
     snake_case											=							func(UpperCamelCase_							)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(UpperCamelCase_							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 def      UpperCAmelCase__		(UpperCamelCase_							):



      """simple docstring"""






      return y**2 * y**4

 print(differentiate(f, 9, 2))




